Overview

Dataset statistics

Number of variables12
Number of observations1359
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory138.0 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

fixed acidity is highly overall correlated with citric acid and 2 other fieldsHigh correlation
volatile acidity is highly overall correlated with citric acidHigh correlation
citric acid is highly overall correlated with fixed acidity and 2 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxideHigh correlation
density is highly overall correlated with fixed acidityHigh correlation
pH is highly overall correlated with fixed acidity and 1 other fieldsHigh correlation
citric acid has 118 (8.7%) zerosZeros

Reproduction

Analysis started2023-04-24 14:29:46.375021
Analysis finished2023-04-24 14:30:06.039427
Duration19.66 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

Distinct96
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.310596
Minimum4.6
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2023-04-24T16:30:06.150859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile6.1
Q17.1
median7.9
Q39.2
95-th percentile11.71
Maximum15.9
Range11.3
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.7369898
Coefficient of variation (CV)0.20900905
Kurtosis1.0496734
Mean8.310596
Median Absolute Deviation (MAD)1
Skewness0.94104137
Sum11294.1
Variance3.0171336
MonotonicityNot monotonic
2023-04-24T16:30:06.317724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.2 49
 
3.6%
7.8 48
 
3.5%
7.1 46
 
3.4%
7 44
 
3.2%
7.5 42
 
3.1%
7.6 41
 
3.0%
7.7 40
 
2.9%
7.9 38
 
2.8%
6.8 38
 
2.8%
8 37
 
2.7%
Other values (86) 936
68.9%
ValueCountFrequency (%)
4.6 1
 
0.1%
4.7 1
 
0.1%
4.9 1
 
0.1%
5 6
0.4%
5.1 4
 
0.3%
5.2 5
0.4%
5.3 4
 
0.3%
5.4 5
0.4%
5.5 1
 
0.1%
5.6 11
0.8%
ValueCountFrequency (%)
15.9 1
0.1%
15.6 2
0.1%
15.5 1
0.1%
15 1
0.1%
14.3 1
0.1%
14 1
0.1%
13.8 1
0.1%
13.7 1
0.1%
13.5 1
0.1%
13.4 1
0.1%

volatile acidity
Real number (ℝ)

Distinct143
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52947756
Minimum0.12
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2023-04-24T16:30:06.465923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.27
Q10.39
median0.52
Q30.64
95-th percentile0.8505
Maximum1.58
Range1.46
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.18303132
Coefficient of variation (CV)0.34568286
Kurtosis1.2492435
Mean0.52947756
Median Absolute Deviation (MAD)0.125
Skewness0.72927895
Sum719.56
Variance0.033500463
MonotonicityNot monotonic
2023-04-24T16:30:06.608810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 37
 
2.7%
0.58 36
 
2.6%
0.4 35
 
2.6%
0.6 34
 
2.5%
0.43 33
 
2.4%
0.39 31
 
2.3%
0.59 31
 
2.3%
0.38 31
 
2.3%
0.42 30
 
2.2%
0.49 30
 
2.2%
Other values (133) 1031
75.9%
ValueCountFrequency (%)
0.12 1
 
0.1%
0.16 2
 
0.1%
0.18 7
0.5%
0.19 2
 
0.1%
0.2 3
 
0.2%
0.21 5
0.4%
0.22 5
0.4%
0.23 5
0.4%
0.24 11
0.8%
0.25 7
0.5%
ValueCountFrequency (%)
1.58 1
 
0.1%
1.33 2
0.1%
1.24 1
 
0.1%
1.185 1
 
0.1%
1.18 1
 
0.1%
1.13 1
 
0.1%
1.115 1
 
0.1%
1.09 1
 
0.1%
1.07 1
 
0.1%
1.04 3
0.2%

citric acid
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2723326
Minimum0
Maximum1
Zeros118
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2023-04-24T16:30:06.766676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.09
median0.26
Q30.43
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.19553654
Coefficient of variation (CV)0.71800639
Kurtosis-0.7889205
Mean0.2723326
Median Absolute Deviation (MAD)0.17
Skewness0.31272554
Sum370.1
Variance0.03823454
MonotonicityNot monotonic
2023-04-24T16:30:06.917961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118
 
8.7%
0.49 59
 
4.3%
0.24 41
 
3.0%
0.02 38
 
2.8%
0.08 32
 
2.4%
0.26 30
 
2.2%
0.1 29
 
2.1%
0.4 27
 
2.0%
0.32 26
 
1.9%
0.31 26
 
1.9%
Other values (70) 933
68.7%
ValueCountFrequency (%)
0 118
8.7%
0.01 25
 
1.8%
0.02 38
 
2.8%
0.03 24
 
1.8%
0.04 24
 
1.8%
0.05 18
 
1.3%
0.06 20
 
1.5%
0.07 17
 
1.3%
0.08 32
 
2.4%
0.09 25
 
1.8%
ValueCountFrequency (%)
1 1
 
0.1%
0.79 1
 
0.1%
0.78 1
 
0.1%
0.76 3
0.2%
0.75 1
 
0.1%
0.74 3
0.2%
0.73 2
0.1%
0.72 1
 
0.1%
0.71 1
 
0.1%
0.7 2
0.1%

residual sugar
Real number (ℝ)

Distinct91
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5233996
Minimum0.9
Maximum15.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2023-04-24T16:30:07.066974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.6
Q11.9
median2.2
Q32.6
95-th percentile4.8
Maximum15.5
Range14.6
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation1.3523138
Coefficient of variation (CV)0.53590948
Kurtosis29.364592
Mean2.5233996
Median Absolute Deviation (MAD)0.3
Skewness4.5481534
Sum3429.3
Variance1.8287525
MonotonicityNot monotonic
2023-04-24T16:30:07.210882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 133
 
9.8%
2.2 110
 
8.1%
1.8 108
 
7.9%
2.1 104
 
7.7%
1.9 97
 
7.1%
2.3 86
 
6.3%
2.5 74
 
5.4%
2.4 74
 
5.4%
2.6 71
 
5.2%
1.7 62
 
4.6%
Other values (81) 440
32.4%
ValueCountFrequency (%)
0.9 1
 
0.1%
1.2 7
 
0.5%
1.3 5
 
0.4%
1.4 29
 
2.1%
1.5 25
 
1.8%
1.6 56
4.1%
1.65 2
 
0.1%
1.7 62
4.6%
1.75 2
 
0.1%
1.8 108
7.9%
ValueCountFrequency (%)
15.5 1
0.1%
15.4 1
0.1%
13.9 1
0.1%
13.8 1
0.1%
13.4 1
0.1%
12.9 1
0.1%
11 1
0.1%
10.7 1
0.1%
9 1
0.1%
8.9 1
0.1%

chlorides
Real number (ℝ)

Distinct153
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08812362
Minimum0.012
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2023-04-24T16:30:07.365288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.012
5-th percentile0.053
Q10.07
median0.079
Q30.091
95-th percentile0.1376
Maximum0.611
Range0.599
Interquartile range (IQR)0.021

Descriptive statistics

Standard deviation0.049376862
Coefficient of variation (CV)0.56031359
Kurtosis38.624653
Mean0.08812362
Median Absolute Deviation (MAD)0.01
Skewness5.5024873
Sum119.76
Variance0.0024380745
MonotonicityNot monotonic
2023-04-24T16:30:07.502926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 50
 
3.7%
0.078 44
 
3.2%
0.074 43
 
3.2%
0.084 40
 
2.9%
0.076 39
 
2.9%
0.079 39
 
2.9%
0.082 38
 
2.8%
0.075 37
 
2.7%
0.071 36
 
2.6%
0.077 36
 
2.6%
Other values (143) 957
70.4%
ValueCountFrequency (%)
0.012 1
 
0.1%
0.034 1
 
0.1%
0.038 2
 
0.1%
0.039 4
0.3%
0.041 4
0.3%
0.042 3
0.2%
0.043 1
 
0.1%
0.044 5
0.4%
0.045 4
0.3%
0.046 4
0.3%
ValueCountFrequency (%)
0.611 1
0.1%
0.61 1
0.1%
0.467 1
0.1%
0.464 1
0.1%
0.422 1
0.1%
0.415 2
0.1%
0.414 2
0.1%
0.413 1
0.1%
0.403 1
0.1%
0.401 1
0.1%

free sulfur dioxide
Real number (ℝ)

Distinct60
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.893304
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2023-04-24T16:30:07.653126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median14
Q321
95-th percentile35
Maximum72
Range71
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.44727
Coefficient of variation (CV)0.65733785
Kurtosis1.8926907
Mean15.893304
Median Absolute Deviation (MAD)7
Skewness1.2265795
Sum21599
Variance109.14546
MonotonicityNot monotonic
2023-04-24T16:30:07.796826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 121
 
8.9%
5 88
 
6.5%
15 65
 
4.8%
12 64
 
4.7%
10 63
 
4.6%
7 61
 
4.5%
9 55
 
4.0%
16 53
 
3.9%
17 50
 
3.7%
11 49
 
3.6%
Other values (50) 690
50.8%
ValueCountFrequency (%)
1 2
 
0.1%
2 1
 
0.1%
3 41
 
3.0%
4 34
 
2.5%
5 88
6.5%
5.5 1
 
0.1%
6 121
8.9%
7 61
4.5%
8 47
 
3.5%
9 55
4.0%
ValueCountFrequency (%)
72 1
 
0.1%
68 1
 
0.1%
66 1
 
0.1%
57 1
 
0.1%
55 1
 
0.1%
54 1
 
0.1%
53 1
 
0.1%
52 3
0.2%
51 3
0.2%
50 2
0.1%

total sulfur dioxide
Real number (ℝ)

Distinct144
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.825975
Minimum6
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2023-04-24T16:30:07.951576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q122
median38
Q363
95-th percentile113
Maximum289
Range283
Interquartile range (IQR)41

Descriptive statistics

Standard deviation33.408946
Coefficient of variation (CV)0.71347037
Kurtosis4.0422567
Mean46.825975
Median Absolute Deviation (MAD)19
Skewness1.5403681
Sum63636.5
Variance1116.1577
MonotonicityNot monotonic
2023-04-24T16:30:08.093401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 35
 
2.6%
24 32
 
2.4%
14 30
 
2.2%
20 29
 
2.1%
18 28
 
2.1%
15 28
 
2.1%
19 27
 
2.0%
23 27
 
2.0%
12 26
 
1.9%
13 25
 
1.8%
Other values (134) 1072
78.9%
ValueCountFrequency (%)
6 2
 
0.1%
7 4
 
0.3%
8 11
 
0.8%
9 13
1.0%
10 23
1.7%
11 22
1.6%
12 26
1.9%
13 25
1.8%
14 30
2.2%
15 28
2.1%
ValueCountFrequency (%)
289 1
0.1%
278 1
0.1%
165 1
0.1%
160 1
0.1%
155 1
0.1%
153 1
0.1%
152 1
0.1%
151 2
0.1%
149 1
0.1%
148 2
0.1%

density
Real number (ℝ)

Distinct436
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99670895
Minimum0.99007
Maximum1.00369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2023-04-24T16:30:08.250794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.99007
5-th percentile0.993569
Q10.9956
median0.9967
Q30.99782
95-th percentile0.9998
Maximum1.00369
Range0.01362
Interquartile range (IQR)0.00222

Descriptive statistics

Standard deviation0.0018689171
Coefficient of variation (CV)0.0018750881
Kurtosis0.83065876
Mean0.99670895
Median Absolute Deviation (MAD)0.0011
Skewness0.044777856
Sum1354.5275
Variance3.4928512 × 10-6
MonotonicityNot monotonic
2023-04-24T16:30:08.796874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9968 33
 
2.4%
0.9976 30
 
2.2%
0.9972 29
 
2.1%
0.998 28
 
2.1%
0.9962 23
 
1.7%
0.9978 22
 
1.6%
0.9964 22
 
1.6%
0.997 21
 
1.5%
0.9982 21
 
1.5%
0.9966 20
 
1.5%
Other values (426) 1110
81.7%
ValueCountFrequency (%)
0.99007 1
0.1%
0.9902 1
0.1%
0.99064 1
0.1%
0.9908 1
0.1%
0.99084 1
0.1%
0.9912 1
0.1%
0.9915 1
0.1%
0.99154 1
0.1%
0.99157 1
0.1%
0.9916 1
0.1%
ValueCountFrequency (%)
1.00369 1
0.1%
1.0032 1
0.1%
1.00315 2
0.1%
1.00289 1
0.1%
1.0026 2
0.1%
1.00242 1
0.1%
1.0022 1
0.1%
1.0021 1
0.1%
1.0018 1
0.1%
1.0015 1
0.1%

pH
Real number (ℝ)

Distinct89
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3097866
Minimum2.74
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2023-04-24T16:30:08.951944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.74
5-th percentile3.06
Q13.21
median3.31
Q33.4
95-th percentile3.57
Maximum4.01
Range1.27
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.15503631
Coefficient of variation (CV)0.046841785
Kurtosis0.87978974
Mean3.3097866
Median Absolute Deviation (MAD)0.1
Skewness0.23203228
Sum4498
Variance0.024036258
MonotonicityNot monotonic
2023-04-24T16:30:09.105780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3 47
 
3.5%
3.26 45
 
3.3%
3.36 42
 
3.1%
3.38 41
 
3.0%
3.32 40
 
2.9%
3.34 40
 
2.9%
3.39 39
 
2.9%
3.31 37
 
2.7%
3.28 37
 
2.7%
3.22 35
 
2.6%
Other values (79) 956
70.3%
ValueCountFrequency (%)
2.74 1
 
0.1%
2.86 1
 
0.1%
2.87 1
 
0.1%
2.88 2
0.1%
2.89 2
0.1%
2.9 1
 
0.1%
2.92 3
0.2%
2.93 2
0.1%
2.94 3
0.2%
2.95 1
 
0.1%
ValueCountFrequency (%)
4.01 2
0.1%
3.9 2
0.1%
3.85 1
 
0.1%
3.78 2
0.1%
3.75 1
 
0.1%
3.74 1
 
0.1%
3.72 2
0.1%
3.71 3
0.2%
3.7 1
 
0.1%
3.69 2
0.1%

sulphates
Real number (ℝ)

Distinct96
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65870493
Minimum0.33
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2023-04-24T16:30:09.262790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile0.47
Q10.55
median0.62
Q30.73
95-th percentile0.94
Maximum2
Range1.67
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.17066689
Coefficient of variation (CV)0.2590946
Kurtosis11.102282
Mean0.65870493
Median Absolute Deviation (MAD)0.08
Skewness2.4065046
Sum895.18
Variance0.029127188
MonotonicityNot monotonic
2023-04-24T16:30:09.422249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.54 58
 
4.3%
0.58 57
 
4.2%
0.6 57
 
4.2%
0.62 53
 
3.9%
0.56 52
 
3.8%
0.57 48
 
3.5%
0.53 46
 
3.4%
0.59 44
 
3.2%
0.55 42
 
3.1%
0.61 41
 
3.0%
Other values (86) 861
63.4%
ValueCountFrequency (%)
0.33 1
 
0.1%
0.37 2
 
0.1%
0.39 3
 
0.2%
0.4 3
 
0.2%
0.42 4
 
0.3%
0.43 8
0.6%
0.44 12
0.9%
0.45 9
0.7%
0.46 14
1.0%
0.47 17
1.3%
ValueCountFrequency (%)
2 1
 
0.1%
1.98 1
 
0.1%
1.95 1
 
0.1%
1.62 1
 
0.1%
1.61 1
 
0.1%
1.59 1
 
0.1%
1.56 1
 
0.1%
1.36 3
0.2%
1.34 1
 
0.1%
1.33 1
 
0.1%

alcohol
Real number (ℝ)

Distinct65
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.432315
Minimum8.4
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2023-04-24T16:30:09.591676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8.4
5-th percentile9.2
Q19.5
median10.2
Q311.1
95-th percentile12.5
Maximum14.9
Range6.5
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.0820654
Coefficient of variation (CV)0.10372246
Kurtosis0.15973885
Mean10.432315
Median Absolute Deviation (MAD)0.7
Skewness0.85984117
Sum14177.517
Variance1.1708656
MonotonicityNot monotonic
2023-04-24T16:30:09.743552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 111
 
8.2%
9.4 91
 
6.7%
9.2 65
 
4.8%
9.8 63
 
4.6%
10 61
 
4.5%
9.3 56
 
4.1%
10.5 53
 
3.9%
9.6 49
 
3.6%
9.7 47
 
3.5%
11 46
 
3.4%
Other values (55) 717
52.8%
ValueCountFrequency (%)
8.4 2
 
0.1%
8.5 1
 
0.1%
8.7 2
 
0.1%
8.8 1
 
0.1%
9 21
 
1.5%
9.05 1
 
0.1%
9.1 21
 
1.5%
9.2 65
4.8%
9.233333333 1
 
0.1%
9.25 1
 
0.1%
ValueCountFrequency (%)
14.9 1
 
0.1%
14 6
0.4%
13.6 4
0.3%
13.56666667 1
 
0.1%
13.5 1
 
0.1%
13.4 3
0.2%
13.3 3
0.2%
13.2 1
 
0.1%
13.1 2
 
0.1%
13 5
0.4%

quality
Real number (ℝ)

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6232524
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2023-04-24T16:30:09.860182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.823578
Coefficient of variation (CV)0.14645937
Kurtosis0.34025609
Mean5.6232524
Median Absolute Deviation (MAD)1
Skewness0.19240659
Sum7642
Variance0.67828072
MonotonicityNot monotonic
2023-04-24T16:30:09.960313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 577
42.5%
6 535
39.4%
7 167
 
12.3%
4 53
 
3.9%
8 17
 
1.3%
3 10
 
0.7%
ValueCountFrequency (%)
3 10
 
0.7%
4 53
 
3.9%
5 577
42.5%
6 535
39.4%
7 167
 
12.3%
8 17
 
1.3%
ValueCountFrequency (%)
8 17
 
1.3%
7 167
 
12.3%
6 535
39.4%
5 577
42.5%
4 53
 
3.9%
3 10
 
0.7%

Interactions

2023-04-24T16:30:04.204867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:46.623122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:48.054151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:49.574312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:51.097897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:52.757733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:54.433317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:56.382416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:58.042190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:59.631532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:01.121541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:02.697330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:04.321224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:46.737892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:48.173699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:49.689367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:51.239012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:52.884529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:54.554129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:56.501308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:58.197797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:59.751714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:01.242372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:02.813659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:04.447531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:46.859821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:48.300199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:49.817054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:51.370676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:53.025763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:54.687274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:56.638005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:58.329547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:59.879810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:01.374784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:02.944053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:04.558962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:46.976104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:48.422523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:49.930302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:51.498304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:53.157865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:54.819359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:56.773624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:58.465410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:00.003330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:01.507999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:03.061127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:04.683230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:47.098539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:48.553704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:50.051834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:51.641422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:53.299257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:54.962152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:56.910900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:58.597817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:00.136763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:01.635675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:03.189157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:04.802696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:47.217080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:48.679969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:50.173484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:51.779898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:53.432887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:55.096835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:57.088768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:58.725261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:00.258313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:01.762662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:03.312211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:04.926611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:47.335748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:48.807188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:50.298896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:51.921958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:53.582022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:55.234902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:57.231498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:58.851512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:00.384194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:01.896264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:03.441272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:05.046537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:47.458947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:48.931539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:50.496734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:52.093501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:53.711977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:55.372592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:57.361825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:58.977992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:00.506844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:02.021255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:03.580203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:05.175225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:47.583929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:49.063874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:50.620754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:52.226646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:53.854184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:55.523766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:57.497879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:59.108156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:00.638839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:02.154984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:03.711112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:05.294640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:47.702412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:49.193103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:50.738880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:52.354949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:54.041423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:55.666898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:57.635577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:59.237951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:00.759534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:02.281155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:03.833742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:05.418442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:47.825490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:49.325467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:50.861958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:52.490257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:54.183576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:56.105986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:57.771818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:59.377492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:00.887548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:02.418641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:03.964436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:05.540231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:47.943192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:49.454162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:50.981594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:52.628574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:54.314525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:56.261339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:57.900608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:29:59.503809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:01.007949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:02.575793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T16:30:04.082699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-24T16:30:10.077706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
fixed acidity1.000-0.2800.6570.2220.244-0.158-0.0840.627-0.7090.221-0.0630.112
volatile acidity-0.2801.000-0.6110.0310.1710.0120.0930.0320.246-0.324-0.226-0.387
citric acid0.657-0.6111.0000.1700.111-0.0600.0210.344-0.5590.3420.0910.219
residual sugar0.2220.0310.1701.0000.2160.0780.1420.414-0.0920.0300.1120.026
chlorides0.2440.1710.1110.2161.0000.0100.1410.421-0.2340.034-0.302-0.204
free sulfur dioxide-0.1580.012-0.0600.0780.0101.0000.790-0.0260.0910.041-0.099-0.059
total sulfur dioxide-0.0840.0930.0210.1420.1410.7901.0000.137-0.030-0.008-0.275-0.197
density0.6270.0320.3440.4140.421-0.0260.1371.000-0.3230.158-0.470-0.184
pH-0.7090.246-0.559-0.092-0.2340.091-0.030-0.3231.000-0.1000.183-0.043
sulphates0.221-0.3240.3420.0300.0340.041-0.0080.158-0.1001.0000.2080.381
alcohol-0.063-0.2260.0910.112-0.302-0.099-0.275-0.4700.1830.2081.0000.488
quality0.112-0.3870.2190.026-0.204-0.059-0.197-0.184-0.0430.3810.4881.000

Missing values

2023-04-24T16:30:05.730849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-24T16:30:05.949969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
07.40.700.001.90.07611.034.00.99783.510.569.45
17.80.880.002.60.09825.067.00.99683.200.689.85
27.80.760.042.30.09215.054.00.99703.260.659.85
311.20.280.561.90.07517.060.00.99803.160.589.86
57.40.660.001.80.07513.040.00.99783.510.569.45
67.90.600.061.60.06915.059.00.99643.300.469.45
77.30.650.001.20.06515.021.00.99463.390.4710.07
87.80.580.022.00.0739.018.00.99683.360.579.57
97.50.500.366.10.07117.0102.00.99783.350.8010.55
106.70.580.081.80.09715.065.00.99593.280.549.25
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
15887.20.6600.332.50.06834.0102.00.994143.270.7812.86
15896.60.7250.207.80.07329.079.00.997703.290.549.25
15906.30.5500.151.80.07726.035.00.993143.320.8211.66
15915.40.7400.091.70.08916.026.00.994023.670.5611.66
15926.30.5100.132.30.07629.040.00.995743.420.7511.06
15936.80.6200.081.90.06828.038.00.996513.420.829.56
15946.20.6000.082.00.09032.044.00.994903.450.5810.55
15955.90.5500.102.20.06239.051.00.995123.520.7611.26
15975.90.6450.122.00.07532.044.00.995473.570.7110.25
15986.00.3100.473.60.06718.042.00.995493.390.6611.06